{"data":{"slug":"hallucination","etype":"concept","name":"Hallucination","cat":"Training & Inference","subtitle":"Confident fabrication — the failure mode that shapes deployment.","summary":"Models sometimes produce fluent falsehoods, because next-token prediction optimizes plausibility, not truth. Mitigations — RAG grounding, citations, verifier models, calibrated refusals — reduce but do not eliminate it. It is the single biggest brake on trusting models in medicine, law and finance, and the reason this site labels every figure with its source.","attrs":null,"kf":null,"aliases":["hallucinations","confabulation"],"ticker":null,"exchange":null,"hq":null,"founded":null,"website":null,"status":null,"region":null,"asof":"2026-01-15","sources":[{"url":"https://openai.com/index/why-language-models-hallucinate/","label":"OpenAI on why models hallucinate"}]},"meta":{"found":true,"count":1,"served_from":"site cache (no live upstream calls)","as_of":"2026-10-14T04:30:38+00:00","disclaimer":"Research and information only — not investment advice. Structured records carry basis labels (vendor spec / reported / estimate); market quotes are delayed/indicative.","attribution":"Records compiled from vendor documentation, official filings and reputable public reporting; market quotes via financial data providers. See /data-sources."}}